State-of-the-art approaches to AED are most commonly based on deep learning [1]} which requires large centralized datasets for model training, posing significant security and logistical challenges. FL [2]} offers an attractive approach to mitigate some of these concerns. Instead of sending private data to a centralized data store, FL performs model training directly on many client edge devices (from here on referred to as clients) using locally stored data. The clients then share only their updated parameters with a coordination server, which aggregates the shared parameters to update a global model. The new global model is then transferred back to the clients. This process continues until convergence, or indefinitely if new data is continuously acquired [3]}.
